1. Field
One or more embodiments of the present invention relate to an image-processing technique, and, more particularly, to an apparatus and method for restoring an image in which a valid value corresponding to a distance between a subject of an input image and an image sensor is calculated based on a red (R)-channel edge strength, a green (G)-channel edge strength and a blue (B)-channel edge strength of the input image, and it is determined which of R, G and B channels is sharpest based on the valid value and a point spread function (PSF), corresponding to whichever of the R, G and B channels is determined to be sharpest.
2. Description of the Related Art
A deterioration of focus caused by an imprecise adjustment of focal distance is one of the major causes of the deteriorating quality of pictures obtained by image-capturing devices. In order to prevent such deterioration of focus, image-capturing devices equipped with auto-focusing (AF) systems have been commercialized.
In particular, an infrared AF (IRAF) method and a semi-digital AF (SDAF) method have been widely used in conventional image-processing systems. The IRAF method involves determining whether focus is correct, based on the distance traveled by the light with the aid of an interpretation module, and performing focus adjustment by moving a lens according to the result of the determination with the aid of a control module. The SDAF method involves determining whether the focus is correct by calculating high-frequency components with the aid of an interpretation module, and performing focus adjustment, by moving a lens, according to the results of the calculation with the aid of a control module. However, the IRAF method and the SDAF method may not be able to ensure reliability for the operation of an interpretation module, and may require a motor driving module of a moving a lens under the control of a control module. In addition, it is generally time consuming to obtain an in-focus image using the IRAF method or the SDAF method.
In order to address the problems associated with the IRAF method or the SDAF method, the necessity of the replacement of the IRAF method or the SDAF method with a fully digital AF (FDAF) method has arisen. The FDAF method is characterized by restoring an image according to the properties of an image-processing system without the need to drive a lens.
Specifically, in the FDAF method, an interpretation module and a control module may both be realized through digital signal processing. The FDAF method involves estimating a point spread function (PSF) of an input image, instead of measuring the degree of focus with the aid of an interpretation module. The FDAF method also involves providing an in-focus image using a PSF estimated by an interpretation module and using an image restoration technique with the aid of a control module.
In general, in order to estimate a PSF appropriate for an input image, a blur level of the input image may be estimated. Conventionally, a variety of blur estimation methods have been used to estimate a blur level of an input image.
Specifically, a blur level of an input image may be estimated by determining an unknown blur radius based on differences between the input image and a plurality of images. This method, however, may result in errors caused by an input image having a complicated shape, or due to noise. Also, this method may not be able to estimate a PSF corresponding to a distance-specific blur level.
Alternatively, a blur level of an input image may be estimated by detecting red (R), green (G) and blue (B) channels from the input image and comparing the R, G and B channels. This method involves analyzing the correlation between a sharp channel and a blurry channel, and estimating a blur kernel based on the result of the analysis. Still alternatively, a blur level of an input image may be estimated by comparing first and second color channels of an input image, which are classified as high-frequency features of the input image, and extracting information lost from the second color channel due to a blur phenomenon.
However, the above-mentioned blur estimation methods may all result in errors, especially when an input image has a complicated shape. Also, the above-mentioned blur estimation methods may not be able to estimate a PSF corresponding to a distance-specific blur level, even though they are able to determine the relative differences between the blur levels of channels.
Therefore, it is very important to precisely estimate a distance-specific PSF for various images.